April 2024 arXiv papers — page 79

Z. Haba

We study the Schr\"odinger equation in quantum field theory (QFT) in its functional formulation. In this approach quantum correlation functions can be expressed as classical expectation values over (complex) stochastic processes. We obtain a stochastic representation of the Schr\"odinger time evolution on Wentzel-Kramers-Brillouin (WKB) states by means of th

Filiz Çağatay Uçgun, Oğul Esen, Serkan Sütlü

We present the Euler-Lagrange and Hamilton's equations for a system whose configuration space is a unified product Lie group $G=M\bowtie_{\gamma} H$, for some $\gamma:M\times M \to H$. By reduction, then, we obtain the Euler-Lagrange type and Hamilton's type equations of the same form for the quotient space $M\cong G/H$, although it is not necessarily a Lie

Jonathon Riddell, Bruno Bertini

Rationally independent free fermions are those where sums of single-particle energies multiplied by arbitrary rational coefficients vanish only if the coefficients are all zero. This property guaranties that they have no degeneracies in the many-body spectrum and gives them relaxation properties more similar to those of generic systems. Using classic results

Manda Malekpour, Kourosh Nozari

The discovery of Higgs mechanism within the context of spontaneous symmetry breaking has offered a new perspective on the early time cosmic inflation and also on the relationship between elementary particles and dark energy, believed to drive the universe's accelerating expansion. We suggest an inflation scenario driven by the Higgs boson within the framewor

Nil Mansuroglu, Bouzid Mosbahi

BiHom-superdialgebras are clear generalization of Hom-superdialgebras. The purpose of this note is to describe and to survey structures of BiHom-superdialgebras. Then we derive derivations of BiHomsuperdialgebras.

Jiaqi Yan, Ankush Chakrabarty, Alisa Rupenyan, John Lygeros

In this paper, we consider the problem of reference tracking in uncertain nonlinear systems. A neural State-Space Model (NSSM) is used to approximate the nonlinear system, where a deep encoder network learns the nonlinearity from data, and a state-space component captures the temporal relationship. This transforms the nonlinear system into a linear system in

Dawei Zhu, Liang Wang, Nan Yang, Yifan Song

Embedding models play a pivot role in modern NLP applications such as IR and RAG. While the context limit of LLMs has been pushed beyond 1 million tokens, embedding models are still confined to a narrow context window not exceeding 8k tokens, refrained from application scenarios requiring long inputs such as legal contracts. This paper explores context windo

Angshuman Robin Goswami, István Szalkai

In this paper, we deal with the question; under what conditions the points $P_i(xi,yi)$ $(i = 1,\cdots, n)$ form a convex polygon provided $x_1 < \cdots < x_n$ holds. One of the main findings of the paper can be stated as follows: "Let $P_1(x_1,y_1),\cdots ,P_n(x_n,y_n)$ are $n$ distinct points ($n\geq3$) with $x_1<\cdots<x_n$. Then $\overline{P_1P_2},\cdots

Francesco Di Clemente, Marco Casolino, Alessandro Drago, Massimiliano Lattanzi

Forty years ago Witten suggested that dark matter could be composed of macroscopic clusters of strange quark matter. This idea was very popular for several years, but it dropped out of fashion once lattice QCD calculations indicated that the confinement/deconfinement transition, at small baryonic chemical potential, is not first order, which seemed to be a c

Oleksandr Kuznetsov, Alex Rusnak, Anton Yezhov, Kateryna Kuznetsova

Addressing the critical challenge of ensuring data integrity in decentralized systems, this paper delves into the underexplored area of data falsification probabilities within Merkle Trees, which are pivotal in blockchain and Internet of Things (IoT) technologies. Despite their widespread use, a comprehensive understanding of the probabilistic aspects of dat

Audrey Scognamiglio, Karin S. Thalmann, Sebastian Hartweg, Nicolas Rendler

The ultrafast relaxation dynamics of tetracene following UV excitation to a bright singlet state S6 has been studied with time-resolved photoelectron spectroscopy. With the help of high-level ab-initio multireference perturbation theory calculations, we assign photoelectron signals to intermediate dark electronic states S3, S4 and S5 as well as a to a low-ly

Wu Ran, Peirong Ma, Zhiquan He, Hao Ren

Recent advances in image deraining have focused on training powerful models on mixed multiple datasets comprising diverse rain types and backgrounds. However, this approach tends to overlook the inherent differences among rainy images, leading to suboptimal results. To overcome this limitation, we focus on addressing various rainy images by delving into mean

Haoyuan Jiang, Ziyue Li, Hua Wei, Xuantang Xiong

The effectiveness of traffic light control has been significantly improved by current reinforcement learning-based approaches via better cooperation among multiple traffic lights. However, a persisting issue remains: how to obtain a multi-agent traffic signal control algorithm with remarkable transferability across diverse cities? In this paper, we propose a

Hamidreza Taghvaee, Mohsen Khalily, Gabriele Gradoni, Rahim Tafazolli

This study delves into the innovative landscape of metasurfaces, with a particular focus on their role in achieving EM illusion (EMI) a facet of paramount significance. The control of EM waves assumes a pivotal role in mitigating issues such as signal degradation, interference, and reduced communication range. Furthermore, the engineering of waves serves as

R. Alessa, R. Al Subaie, M. Alwohaibi, M. Majdoub

We investigate the fractional Hardy-H\'enon equation with fractional Brownian noise $$ \partial_tu(t)+(-\Delta)^{\theta/2} u(t)=|x|^{-\gamma} |u(t)|^{p-1}u(t)+\mu \, \partial_t B^H(t), $$ where $\theta>0$, $p>1$, $\gamma\geq 0$, $\mu \in\mathbb{R}$, and the random forcing $B^H$ is the fractional Brownian motion defined on some complete probability space $(\O

Tony Lelièvre, Grigorios A. Pavliotis, Geneviève Robin, Régis Santet

Overdamped Langevin dynamics are reversible stochastic differential equations which are commonly used to sample probability measures in high-dimensional spaces, such as the ones appearing in computational statistical physics and Bayesian inference. By varying the diffusion coefficient, there are in fact infinitely many overdamped Langevin dynamics which are

Kostas Kryptos Chalkias, Angelos Kostis, Ali Alnuaimi, Peter Knez

In the contemporary era, biodiversity conservation emerges as a paramount challenge, necessitating innovative approaches to monitoring, preserving, and enhancing the natural world. This paper explores the integration of blockchain technology in biodiversity conservation, offering a novel perspective on how digital resilience can be built within ecological co

Janko Boehm, Wolfram Decker, Frank-Olaf Schreyer

We give illustrative examples of how the computer algebra system OSCAR can support research in commutative algebra and algebraic geometry. We start with a thorough introduction to Groebner basis techniques, with particular emphasis on the computation of syzygies, then apply these techniques to deal with ideal and ring theoretic concepts such as primary decom

Angshuman Robin Goswami

In this paper, our primary objective is to study a possible decomposition of an approximately convex sequence. For a given $\varepsilon>0$; a sequence $\big<u_n\big>_{n=0}^{\infty}$ is said to be $\varepsilon$-convex, if for any $i,j\in\mathbb{N}$ with $i<j$ there exists an $n\in]i,j]\cap \mathbb{N}$ such that the following discrete functional inequality hol

Abhijit Talukdar, Sanjeev Kalita

$f(R)$ gravity is one of the serious alternatives of general relativity having a large range of astronomical consequences. In this work, we study Big Bang Nucleosynthesis (BBN) in $f(R)$ gravity theory. We consider modification to gravity due to the existence of primordial black holes in the radiation era which introduce additional degrees of freedom known a

Zhong Wang, Zengyu Wan, Han Han, Bohao Liao

Event-based eye tracking has shown great promise with the high temporal resolution and low redundancy provided by the event camera. However, the diversity and abruptness of eye movement patterns, including blinking, fixating, saccades, and smooth pursuit, pose significant challenges for eye localization. To achieve a stable event-based eye-tracking system, t

Manuel Bodirsky, Santiago Guzmán-Pro

In this paper, we introduce the generic circular triangle-free graph $\mathbb C_3$ and propose a finite axiomatization of its first order theory. In particular, our main results show that a countable graph $G$ embeds into $\mathbb C_3$ if and only if it is a $\{K_3, K_1 + 2K_2, K_1+C_5, C_6\}$-free graph. As a byproduct of this result, we obtain a geometric

Weikang Yu, Xiaokang Zhang, Samiran Das, Xiao Xiang Zhu

Change detection (CD) from remote sensing (RS) images using deep learning has been widely investigated in the literature. It is typically regarded as a pixel-wise labeling task that aims to classify each pixel as changed or unchanged. Although per-pixel classification networks in encoder-decoder structures have shown dominance, they still suffer from impreci

Elia Onofri

Graphs are a fundamental abstraction in computer science and discrete mathematics, where information is encoded in their combinatorial structure. Graph-reduction techniques aim at simplifying graphs while preserving selected structural properties, typically by grouping vertices and replacing each group with a representative, yielding a contracted graph. A co

Hyunwoo Park

Traditional trajectory planning methods for autonomous vehicles have several limitations. For example, heuristic and explicit simple rules limit generalizability and hinder complex motions. These limitations can be addressed using reinforcement learning-based trajectory planning. However, reinforcement learning suffers from unstable learning, and existing re

Ramy Rashad, Stefano Stramigioli

In this paper we present a novel approach to the geometric formulation of solid and fluid mechanics within the port-Hamiltonian framework, which extends the standard Hamiltonian formulation to non-conservative and open dynamical systems. Leveraging Dirac structures, instead of symplectic or Poisson structures, this formalism allows the incorporation of energ

Rong Wang, Kun Sun

This study employs deep learning techniques to explore four speaker profiling tasks on the TIMIT dataset, namely gender classification, accent classification, age estimation, and speaker identification, highlighting the potential and challenges of multi-task learning versus single-task models. The motivation for this research is twofold: firstly, to empirica

Pivithuru Thejan Amarasinghe, Diem Pham, Binh Tran, Su Nguyen

This paper introduces a novel approach, evolutionary multi-objective optimisation for fairness-aware self-adjusting memory classifiers, designed to enhance fairness in machine learning algorithms applied to data stream classification. With the growing concern over discrimination in algorithmic decision-making, particularly in dynamic data stream environments

Louis-Henri Merino, Alaleh Azhir, Haoqian Zhang, Simone Colombo

Online voting is attractive for convenience and accessibility, but is more susceptible to voter coercion and vote buying than in-person voting. One mitigation is to give voters fake voting credentials that they can yield to a coercer. Fake credentials appear identical to real ones, but cast votes that are silently omitted from the final tally. An important u

Yves Annanias, Daniel Wiegreffe

Regional planning processes and associated redevelopment projects can be complex due to the vast amount of diverse data involved. However, all of this data shares a common geographical reference, especially in the renaturation of former open-cast mining areas. To ensure safety, it is crucial to maintain a comprehensive overview of the interrelated data and d

Tarik P. Cysne, R. B. Muniz, Tatiana G. Rappoport

We show that orbital currents can describe the transport of orbital magnetic moments of Bloch states in models where the formalism based on valley current is not applicable. As a case study, we consider Kekul\'e distorted graphene. We begin by analyzing the band structure in detail and obtain the orbital magnetic moment operator for this model within the fra

Roya Gholamipourfard, Amirhossein Ghazisaeidi, Ruby Stella Bravo Ospina

We propose a theoretical framework to compute, rapidly and accurately, the signal-to-noise ratio at the output of spatial-division multiplexing (SDM) linear MIMO equalizers with arbitrary numbers of spatial modes and filter taps and demonstrate three orders of magnitude of speed-up compared to Monte Carlo simulations.

Wojciech Anyszka

Observable operator models (OOMs) offer a powerful framework for modelling stochastic processes, surpassing the traditional hidden Markov models (HMMs) in generality and efficiency. However, using OOMs to model infinite-dimensional processes poses significant theoretical challenges. This article explores a rigorous approach to developing an approximation the

Vladislav Zheligovsky

We consider solenoidal space-periodic space-analytic solutions to the equations of magnetohydrodynamics. An elementary bound shows that due to the special structure of the nonlinear terms in the equations for modified solutions, effectively they lack a half of the spatial gradient, which appears to be a novel mechanism for depletion of nonlinearity. We prese

Sebastian Barzaghi, Ivan Heibi, Arianna Moretti, Silvio Peroni

As a result of the proliferation of 3D digitisation in the context of cultural heritage projects, digital assets and digitisation processes - being considered as proper research objects - must prioritise adherence to FAIR principles. Existing standards and ontologies, such as CIDOC CRM, play a crucial role in this regard, but they are often over-engineered f

Youri Carloni, Orlando Luongo, Marco Muccino

We investigate the impact of the Dark Energy Spectroscopic Instrument (DESI) 2024 data on dark energy scenarios. We thus analyze three typologies of models, the first in which the cosmic speed up is related to thermodynamics, the second associated with Taylor expansions of the barotropic factor, whereas the third based on \emph{ad hoc} dark energy parameteri

N. Ajaber, A. Alshehri, H. Altamimi, M. Majdoub

This paper focuses on studying the long-time dynamics of the subordination process for a range of linear evolution equations, with a special emphasis on the fractional heat equation. By treating inverse subordinators as random time variables and employing the subordination principle to solve forward Kolmogorov equations, we explore the behavior of the soluti

Muneto Nitta, Shin Sasaki

A solitonic ground state called a chiral soliton lattice (CSL) is realized in a supersymmetric theory with background magnetic field and finite chemical potential. To this end, we construct, in the superfield formalism, a supersymmetric chiral sine-Gordon model as a neutral pion sector of a supersymmetric two-flavor chiral Lagrangian with a Wess-Zumino-Witte

M. Abdul Khaliq, P. Chang, M. Ma, B. Pflugfelder

The escalating challenge of misinformation, particularly in political discourse, requires advanced fact-checking solutions; this is even clearer in the more complex scenario of multimodal claims. We tackle this issue using a multimodal large language model in conjunction with retrieval-augmented generation (RAG), and introduce two novel reasoning techniques:

Charles Gaydon, Floryne Roche

Knowledge of tree species distribution is fundamental to managing forests. New deep learning approaches promise significant accuracy gains for forest mapping, and are becoming a critical tool for mapping multiple tree species at scale. To advance the field, deep learning researchers need large benchmark datasets with high-quality annotations. To this end, we

Thivin Anandh, Divij Ghose, Himanshu Jain, Sashikumaar Ganesan

Variational Physics-Informed Neural Networks (VPINNs) utilize a variational loss function to solve partial differential equations, mirroring Finite Element Analysis techniques. Traditional hp-VPINNs, while effective for high-frequency problems, are computationally intensive and scale poorly with increasing element counts, limiting their use in complex geomet

Jinwu Wang, Wei Mao, Miaomiao Liu

In this paper, we introduce a MusIc conditioned 3D Dance GEneraTion model, named MIDGET based on Dance motion Vector Quantised Variational AutoEncoder (VQ-VAE) model and Motion Generative Pre-Training (GPT) model to generate vibrant and highquality dances that match the music rhythm. To tackle challenges in the field, we introduce three new components: 1) a

Adrián M. González Pérez, Javier Parcet, Jorge Pérez García

Let $\mathbf{E}_n: \mathcal{M} \to \mathcal{M}_n$ and $\mathbf{E}_m: \mathcal{N} \to \mathcal{N}_m$ be two sequences of conditional expectations on finite von Neumann algebras. The optimal weak Orlicz type of the associated strong maximal operator $\mathcal{E} = (\mathbf{E}_n\otimes \mathbf{E}_m)_{n,m}$ is not yet known. In a recent work of Jose Conde and th

Shiqi Zeng, Xiaoli Xu, Yong Zeng

Predictive millimeter-wave (mmWave) beamforming is a promising technique to enable low-latency and high-rate ground-air communications for cellular-connected unmanned aerial vehicles (UAVs). However, the high vulnerability of mmWave to blockages poses practical challenges to the implementation of such a technology. In this paper, we tackle the challenges by

Hasmot Ali, Md. Fahad Hossain, Md. Mehedi Hasan, Sheikh Abujar

Voice based applications are ruling over the era of automation because speech has a lot of factors that determine a speakers information as well as speech. Modern Automatic Speech Recognition (ASR) is a blessing in the field of Human-Computer Interaction (HCI) for efficient communication among humans and devices using Artificial Intelligence technology. Spee

Junjie Chen, Xiangheng He, Danushka Bollegala, Yusuke Miyao

Unsupervised constituency parsing focuses on identifying word sequences that form a syntactic unit (i.e., constituents) in target sentences. Linguists identify the constituent by evaluating a set of Predicate-Argument Structure (PAS) equivalent sentences where we find the constituent appears more frequently than non-constituents (i.e., the constituent corres

Dario D. Monticelli, Fabio Punzo, Jacopo Somaglia

We investigate nonexistence of nontrivial nonnegative solutions to a class of semilinear parabolic equations with a positive potential, posed on weighted graphs. Assuming an upper bound on the Laplacian of the distance and a suitable weighted space-time volume growth condition, we show that no global solutions exists. We also discuss the optimality of the hy

Steffen Holter, Mennatallah El-Assady

As full AI-based automation remains out of reach in most real-world applications, the focus has instead shifted to leveraging the strengths of both human and AI agents, creating effective collaborative systems. The rapid advances in this area have yielded increasingly more complex systems and frameworks, while the nuance of their characterization has gotten

Ziang Ren, Samuel Lensgraf, Alberto Quattrini Li

Accurate localization is fundamental for autonomous underwater vehicles (AUVs) to carry out precise tasks, such as manipulation and construction. Vision-based solutions using fiducial marker are promising, but extremely challenging underwater because of harsh lighting condition underwater. This paper introduces a gradient-based active camera exposure control

Paolo Acampora, Emanuele Cristoforoni

We investigate the behavior of the solution to an elliptic diffraction problem in the union of a smooth set $\Omega$ and a thin layer $\Sigma$ locally described by $\varepsilon h$, where $h$ is a positive function defined on the boundary $\partial\Omega$, and $\varepsilon$ is the ellipticity constant of the differential operator in the thin layer $\Sigma$. W

Antonio Giuseppe Grimaldi, Erica Ipocoana

We study the regularity properties of H\"older continuous minimizers to non-autonomous functionals satisfying $(p,q)$-growth conditions, under Besov assumptions on the coefficients. In particular, we are able to prove higher integrability and higher differentiability results for solutions to our minimum problem.

Eoin Farrell, Gaël Buldgen, Georges Meynet, Patrick Eggenberger

We present a method for a non-linear asteroseismic inversion suitable for gravity-mode pulsators and apply it to slowly pulsating B-type (SPB) stars. Our inversion method is based on the iterative improvement of a parameterised static stellar structure model, which in turn is based on constraints from the observed oscillation periods. We present tests to dem

Marian Aprodu, Yeongrak Kim

We investigate a geometric criterion for a smooth curve $C$ of genus $14$ and degree $18$ to be described as the zero locus of sections in an Ulrich bundle of rank $3$ on a del Pezzo threefold $V_5 \subset \mathbb{P}^6$. The main challenge is to read off the Pfaffian quadrics defining $V_5$ from geometric structures of $C$. We find that this problem is relat

Jimenez Eladio, Hao Wu

Machine Reading Comprehension (MRC) holds a pivotal role in shaping Medical Question Answering Systems (QAS) and transforming the landscape of accessing and applying medical information. However, the inherent challenges in the medical field, such as complex terminology and question ambiguity, necessitate innovative solutions. One key solution involves integr

Naoki Ogawa, Shunta Takahashi, Takashi Tsuda, Takahiro Waki

Recently, there has been a growing interest in celestial holography, which is holography in asymptotically flat spacetimes. This holographic duality exhibits numerous mysterious and fruitful features, particularly on the dual CFT side. In this paper, we present the candidate of dual CFT for Minkowski spacetime extracted from $SL(2,\mathbb{C})/SU(2)\cong H^+_

Chad E. Brown, Mikoláš Janota, Mirek Olšák

Motivated by the recent 10 million dollar AIMO challenge, this paper targets the problem of finding all functions conforming to a given specification. This is a popular problem at mathematical competitions and it brings about a number of challenges, primarily, synthesizing the possible solutions and proving that no other solutions exist. Often, there are inf

Johannes Lengler, Konstantin Sturm

The one-fifth rule and its generalizations are a classical parameter control mechanism in discrete domains. They have also been transferred to control the offspring population size of the $(1, \lambda)$-EA. This has been shown to work very well for hill-climbing, and combined with a restart mechanism it was recently shown by Hevia Fajardo and Sudholt to impr

Barbara Alvarez Gonzalez

These proceedings present the observation of the 4-top production process and the latest results of the production of top-quark pairs associated with W and $\gamma$ bosons ($t\bar{t}W$ and $t\bar{t}\gamma$) and b jets ($t\bar{t}b\bar{b}$) at a collision energy of 13 TeV carried out by the CMS and ATLAS Collaborations.

Jiaqi Li, Xiaobo Wang, Wentao Ding, Zihao Wang

We introduce an innovative RAG-based framework with an ever-improving memory. Inspired by humans'pedagogical process, RAM utilizes recursively reasoning-based retrieval and experience reflections to continually update the memory and learn from users' communicative feedback, namely communicative learning. Extensive experiments with both simulated and real use

Xinping Zhou, Yuandeng Shen, Ding Yuan, Rony Keppens

Electromagnetic wave lensing, a common physical phenomenon recognized in visible light for centuries, finds extensive applications in manipulating light in optical systems such as telescopes and cameras. Magnetohydrodynamic wave is a common perturbation phenomenon in the corona. By using high spatio-temporal resolution observations from the Solar Dynamics Ob

Kamil Malinka, Anton Firc, Pavel Loutocký, Jakub Vostoupal

To keep up with the growing number of cyber-attacks and associated threats, there is an ever-increasing demand for cybersecurity professionals and new methods and technologies. Training new cybersecurity professionals is a challenging task due to the broad scope of the area. One particular field where there is a shortage of experts is Ethical Hacking. Due to

Abinew Ali Ayele, Esubalew Alemneh Jalew, Adem Chanie Ali, Seid Muhie Yimam

The prevalence of digital media and evolving sociopolitical dynamics have significantly amplified the dissemination of hateful content. Existing studies mainly focus on classifying texts into binary categories, often overlooking the continuous spectrum of offensiveness and hatefulness inherent in the text. In this research, we present an extensive benchmark

Siya Qi, Lin Gui, Yulan He, Zheng Yuan

The rapid advancement of Large Language Models (LLMs) has brought a pressing challenge: how to reliably assess hallucinations to guarantee model trustworthiness. Although Automatic Hallucination Evaluation (AHE) has become an indispensable component of this effort, the field remains fragmented in its methodologies, limiting both conceptual clarity and practi

Javira Altmann, Peter Skands

Recent measurements at the LHC have revealed heavy-flavour baryon fractions much larger than those observed at LEP, with e.g., LambdaC+/D0 and LambdaB0/B0 reaching ~ 0.5 at low pT. One scenario that has been at least partly successful in predicting observed trends is QCD colour reconnections with string junctions. In previous work, however, the limit of a lo

William Detmold, Marc Illa, William I. Jay, Assumpta Parreño

The low-energy finite-volume spectrum of the two-nucleon system at a quark mass corresponding to a pion mass of $m_\pi \approx 806$ MeV is studied with lattice quantum chromodynamics (LQCD) using variational methods. The interpolating-operator sets used in [Phys.Rev.D 107 (2023) 9, 094508] are extended by including a complete basis of local hexaquark operato

Zhihao Xu, Ruixuan Huang, Changyu Chen, Xiting Wang

Despite careful safety alignment, current large language models (LLMs) remain vulnerable to various attacks. To further unveil the safety risks of LLMs, we introduce a Safety Concept Activation Vector (SCAV) framework, which effectively guides the attacks by accurately interpreting LLMs' safety mechanisms. We then develop an SCAV-guided attack method that ca

Renrong Shao, Wei Zhang, Jianhua Yin, Jun Wang

Data-free knowledge distillation (DFKD) is a promising approach for addressing issues related to model compression, security privacy, and transmission restrictions. Although the existing methods exploiting DFKD have achieved inspiring achievements in coarse-grained classification, in practical applications involving fine-grained classification tasks that req

Limin Zeng, Ang Gao

The system has addressed the error of "Bad character(s) in field Abstract" for no reason. Please refer to manuscript for the full abstract.

Jan Baumeister, Bernd Finkbeiner, Florian Kohn, Florian Löhr

This paper reports on the integration of runtime monitoring into fully-electric aircraft designed by Volocopter, a German aircraft manufacturer of electric multi-rotor helicopters. The runtime monitor recognizes hazardous situations and system faults. Since the correct operation of the monitor is critical for the safety of the aircraft, the development of th

Yogesh Kumar Maurya, Ramit Bhattacharyya, David I. Pontin

Three-dimensional magnetic nulls are the points where magnetic field vanishes and are preferential sites for magnetic reconnection: a process which converts magnetic energy into heat and accelerates charged particles along with a rearrangement of magnetic field lines. In the solar corona, the reconnections manifest as coronal transients including solar flare

Chaohao Yuan, Songyou Li, Geyan Ye, Yikun Zhang

The core challenge of de novo protein design lies in creating proteins with specific functions or properties, guided by certain conditions. Current models explore to generate protein using structural and evolutionary guidance, which only provide indirect conditions concerning functions and properties. However, textual annotations of proteins, especially the

Vivek Mehta, Francesco Petruccione, Utpal Roy

We construct a hybrid quantum-classical approach for the $K$-Nearest Neighbour algorithm, where the information is embedded in a phase-distributed multimode coherent state with the assistance of a single photon. The task of finding the closeness between the data points is delivered by the quantum optical computer, while the sorting and class assignment are p

Matthias Erbar, Zihui He

We study a fuzzy Boltzmann equation, where particles interact via delocalised collisions, in contrast to classical Boltzmann equations. We discuss the existence and uniqueness of solutions and provide a natural variational characterisation by casting the fuzzy Boltzmann equation into the framework of GENERIC systems (General Equations for Non-Equilibrium Rev

Zeliang Ma, Song Yang, Zhe Cui, Zhicheng Zhao

The new trend in multi-object tracking task is to track objects of interest using natural language. However, the scarcity of paired prompt-instance data hinders its progress. To address this challenge, we propose a high-quality yet low-cost data generation method base on Unreal Engine 5 and construct a brand-new benchmark dataset, named Refer-UE-City, which

Ross Drummond, Pablo R Baldivieso-Monasterios, Giorgio Valmorbida

Model predictive control (MPC) for linear systems with quadratic costs and linear constraints is shown to admit an exact representation as an implicit neural network. A method to "unravel" the implicit neural network of MPC into an explicit one is also introduced. As well as building links between model-based and data-driven control, these results emphasize

Dhruv Khatri, Shivani A. Yadav, Chaitanya A. Athale

Quantification of microscopy time-series of in vitro reconstituted motor driven microtubule (MT) transport in 'gliding assays' is typically performed using computational object tracking tools. However, these are limited to non-intersecting and rod-like filaments. Here, we describe a novel computational image-analysis pipeline, KnotResolver, to track image ti

Federico De Santi, Massimiliano Razzano, Francesco Fidecaro, Luca Muccillo

A yet undetected class of GW signals is represented by the close encounters between compact objects in highly-eccentric e~1 orbits, that can occur in binary systems formed in dense environments such as globular clusters. The expected gravitational signals are short-duration pulses that would repeat over a much longer time scale in case of multiple passages a

Alexandre R. Nieto, Rubén Capeáns, Miguel A. F. Sanjuán

In the seminal paper (Phys. Rep. 52, 263, 1979), Boris Chirikov showed that the standard map does not exhibit a boundary to chaos, but rather that there are small islands (islets) of stability for arbitrarily large values of the nonlinear perturbation. In this context, he established that the area of the islets in the phase space and the range of parameter v

Xiaojue Zhu, Yifeng Fu, Marco De Paoli

We present a theory to describe the Nusselt number ($Nu$), corresponding to the heat or mass flux, as a function of the Rayleigh--Darcy number ($Ra$), the ratio of buoyant driving force over diffusive dissipation, in convective porous media flows. First, we derive exact relationships within the system for the kinetic energy and the thermal dissipation rate.

Hector Kohler, Benoit Clement, Thomas Chaffre, Gilles Le Chenadec

Underwater Unmanned Vehicles (UUVs) have to constantly compensate for the external disturbing forces acting on their body. Adaptive Control theory is commonly used there to grant the control law some flexibility in its response to process variation. Today, learning-based (LB) adaptive methods are leading the field where model-based control structures are com

Jingyao Wang, Yunhan Tian, Yuxuan Yang, Xiaoxin Chen

Micro-expressions (MEs) are involuntary movements revealing people's hidden feelings, which has attracted numerous interests for its objectivity in emotion detection. However, despite its wide applications in various scenarios, micro-expression recognition (MER) remains a challenging problem in real life due to three reasons, including (i) data-level: lack o

Mina Aghaei Dinani, Adrian Holzer, Hung Nguyen, Marco Ajmone Marsan

Fully distributed learning schemes such as Gossip Learning (GL) are gaining momentum due to their scalability and effectiveness even in dynamic settings. However, they often imply a high utilization of communication and computing resources, whose energy footprint may jeopardize the learning process, particularly on battery-operated IoT devices. To address th

Pengfei Wu, Jiahao Liu, Zhuocheng Gong, Qifan Wang

Large language models (LLMs) have recently shown remarkable performance across a wide range of tasks. However, the substantial number of parameters in LLMs contributes to significant latency during model inference. This is particularly evident when utilizing autoregressive decoding methods, which generate one token in a single forward process, thereby not fu

N. Lonigro, R. Doyle, J. S. Allcock, B. Lipschultz

2D profiles of electron density and neutral temperature are inferred from multi-delay Coherence Imaging Spectroscopy data of divertor plasmas using a non-linear inversion technique. The inference is based on imaging the spectral line-broadening of Balmer lines and can differentiate between the Doppler and Stark broadening components by measuring the fringe c

Jie Ma, Min Hu, Pinghui Wang, Wangchun Sun

Audio-Visual Question Answering (AVQA) is a complex multi-modal reasoning task, demanding intelligent systems to accurately respond to natural language queries based on audio-video input pairs. Nevertheless, prevalent AVQA approaches are prone to overlearning dataset biases, resulting in poor robustness. Furthermore, current datasets may not provide a precis

Xin-Chen Duan, Raymundo Ramos, Yue-Lin Sming Tsai

We have developed a set of four fully coupled Boltzmann equations to precisely determine the relic density and temperature of dark matter by including three distinct sectors: dark matter, light scalar, and standard model sectors. The intricacies of heat transfer between dark matter (DM) and the standard model sector through a light scalar particle are explor

Andreas Anastasiou, Angelos Zacharia, Savvas Papaioannou, Panayiotis Kolios

This work introduces a cooperative inspection system designed to efficiently control and coordinate a team of distributed heterogeneous UAV agents for the inspection of 3D structures in cluttered, unknown spaces. Our proposed approach employs a two-stage innovative methodology. Initially, it leverages the complementary sensing capabilities of the robots to c

V. Divakar Botcha, Pavan K. Narayanam

A novel, single step and environment friendly solid state approach for reduction of graphene oxide (GO) monolayers has been demonstrated, in which, arachidic acid-GO-arachidic acid (AA-GO-AA) sandwich structure obtained by Langmuir-Blodgett (LB) technique was heat treated at moderate temperatures to obtain RGO sheets. Heat treatment of AA-GO-AA sandwich stru

Mattia Soldani, Pietro Monti-Guarnieri, Alessia Selmi, Nicola Argiolas

A particle traversing a crystal aligned with one of its crystallographic axes experiences a strong electromagnetic field that is constant along the direction of motion over macroscopic distances. For $e^\pm$ and $\gamma$-rays with energies above a few $\mathrm{GeV}$, this field is amplified by the Lorentz boost, to the point of exceeding the Schwinger critic

Claudia Cuttano, Gabriele Rosi, Gabriele Trivigno, Giuseppe Averta

Humans show an innate capability to identify tools to support specific actions. The association between objects parts and the actions they facilitate is usually named affordance. Being able to segment objects parts depending on the tasks they afford is crucial to enable intelligent robots to use objects of daily living. Traditional supervised learning method

Zi Xiong, Lizhi Qing, Yangyang Kang, Jiawei Liu

The widespread use of pre-trained language models (PLMs) in natural language processing (NLP) has greatly improved performance outcomes. However, these models' vulnerability to adversarial attacks (e.g., camouflaged hints from drug dealers), particularly in the Chinese language with its rich character diversity/variation and complex structures, hatches vital

Semih Yagcioglu, Osman Batur İnce, Aykut Erdem, Erkut Erdem

The rise of large-scale multimodal models has paved the pathway for groundbreaking advances in generative modeling and reasoning, unlocking transformative applications in a variety of complex tasks. However, a pressing question that remains is their genuine capability for stronger forms of generalization, which has been largely underexplored in the multimoda

Rafał Tryniecki

For every $k \in \mathbb{N}$ let $f_k:[\frac{1}{k+1}, \frac{1}{k}] \to [0,1]$ be decreasing, linear functions such that $f_k(\frac{1}{k+1}) = 1$ and $f_k(\frac{1}{k}) = 0$, $k = 1, 2, \dots$. We define iterated function system (IFS) $S_n$ by limiting the collection of functions $f_k$ to first n, meaning $S_n = \{f_k \}_{k=1}^n$. Let $J_n$ denote the limit se

Holger Nobach

The use of three extractors, fed by linear feedback shift registers (LFSR) for generating pseudo-random bit streams is investigated. Specifically, a standard LFSR is combined with a von Neumann extractor, a modified LFSR, extended by the all-zero state, is combined with an output logic, which translates every three bits from the LFSR into up to two output bi

Lasal Jayawardena, Prasan Yapa

Paraphrase generation is a pivotal task in natural language processing (NLP). Existing datasets in the domain lack syntactic and lexical diversity, resulting in paraphrases that closely resemble the source sentences. Moreover, these datasets often contain hate speech and noise, and may unintentionally include non-English language sentences. This research int

Hui Zhang, Minbo Yang, Jianjun Zhang, Xuexiu Zhong

This paper is concerned with the Hamiltonian elliptic system in dimension two\begin{equation*}\aligned \left\{ \begin{array}{lll} -\epsilon^2\Delta u+V(x)u=g(v)\ & \text{in}\quad \mathbb{R}^2,\\ -\epsilon^2\Delta v+V(x)v=f(u)\ & \text{in}\quad \mathbb{R}^2, \end{array}\right.\endaligned \end{equation*} where $V\in C(\mathbb{R}^2)$ has local minimum points, a

Siyi Lin, Chongming Gao, Jiawei Chen, Sheng Zhou

Recommendation Systems (RS) are often plagued by popularity bias. When training a recommendation model on a typically long-tailed dataset, the model tends to not only inherit this bias but often exacerbate it, resulting in over-representation of popular items in the recommendation lists. This study conducts comprehensive empirical and theoretical analyses to

Christopher Hacon, Mihai Paun

In this article we prove analogs of Kawamata's canonical bundle formula, Kawamata subadjunction and plt/lc inversion of adjunction for generalized pairs on Kaehler varieties. We also show that a conjecture of BDPPin dimension n-1 implies that the cone theorem holds for any n-dimensional Kaehler generalized klt pair. Along the way, we obtain more complete ver

Qian Li, Cheng Ji, Shu Guo, Yong Zhao

Multi-modal relation extraction (MMRE) is a challenging task that aims to identify relations between entities in text leveraging image information. Existing methods are limited by their neglect of the multiple entity pairs in one sentence sharing very similar contextual information (ie, the same text and image), resulting in increased difficulty in the MMRE

G. Mann, A. M. Veronig, F. Schuller

Solar flares are accompanied by an enhanced emission of electromagnetic waves from the radio up to the gamma-ray range. The associated hard X-ray (HXR) and microwave radiation is generated by energetic electrons, which carry a substantial part of the energy released during a flare. The flare is generally understood as a manifestation of magnetic reconnection